For my machine reading I analyzed the word “person” and its relationship to other words and people in both narratives. Pictured above are the results of the visualization tool that I used, with Hailey on the left and Sam on the right. I was intrigued by Hailey and Sam’s different perspectives and interactions with other people in the novel, so I wanted to examine these trends more closely. To accomplish this, I used the DocuBurst program on the TaPOR site, which takes a text file as an input and creates a visual categorization from a central root word. For my root word, I used person, which is very general, so I could see a wide range of categorizations and words from both narratives. After DocuBurst finished creating the two visualizations, I examined both side-by-side to study their connections.
For the design of the visualization, I knew from the start I wanted something that incorporated circles, since Only Revolutions is filled with “revolutions” of various forms. DocuBurst caught my attention because when I used a general enough category, the graphics began to like eyes. I had wanted to modify the color scheme of the two visualizations to appear more eye-like, making the root word circles black and the categories green/gold to resemble the eyes of the two characters. However, DocuBurst’s coloring options were sadly limited so I was stuck with two gray-scale images. Still, it is also quite aesthetically pleasing because it effectively shows the distribution and frequency of various categories without being full of text and other clutter.
The results of my visualization can be seen above, with the root word in the center and the categories splitting off from it. The shades of grey represent the number of times that a particular word appeared in each narrative, with white representing no occurrences and the darkest shade representing 33 and 36 occurrences of the word in Sam and Hailey’s visualizations, respectively. The size of each category correlates to the number of “children”, or splinter categories, that a particular word has. It was interesting to see the words that have the largest chunks in each visualization. Worker obviously took up a large chunk in both cases, since a significant portion of the narratives dealt with Sam and Hailey’s attempts to find employment. Seeing simpleton have a decently-sized chunk was also quite-amusing, and it sheds some light on how both characters tend to view certain other people. Unfortunately, the images above do not relay the ability to interact with both visualizations. DocuBurst also enabled you to mouse over a word to view other words that were found near it in the narrative, enabling you to connect words by more than just general categorizations. It also allowed you to click on a word and view both DocuBurst’s definition of the word and the section in which the word appeared. Still, the images above convey a lot of the information I used to discuss the strengths and weaknesses of a machine reading in my paper.